Companies around the world are expecting to apply artificial intelligence (AI) within their organisations in the next few years but are lagging in discussions of the ethics around it, new research has found. More than half of the employers questioned in a multi-country opinion survey say their companies do not currently have a written policy on the ethical use of AI or bots, although 21% expressed a definite concern regarding their companies and a potential for the unethical use of AI. Nearly two-thirds (64%) of the employers surveyed expect their companies to be using AI or advanced automation by 2022 to support efficiency in operations, staffing, budgeting or performance, although only 25% are using it now. Yet in spite of this growing trend, 54% of employers questioned say they are not troubled that AI could be used unethically by their companies as a whole or by individual employees (52%). Employees appear more relaxed than their bosses, with only 17% expressing concern about their companies.

A Bellevue-based insurance startup that uses artificial intelligence to sell personalized insurance options has been sold for $2.35 billion to industry giant Prudential Financial. Assurance IQ, an online insurance-tech company that started in 2016, meets its customers through an online portal instead of through a financial adviser. It sells life insurance, auto insurance, health insurance and Medicare options online from over 20 providers. The company says it has sold at least one insurance product to 300,000 customers and the site has had more than 18 million visitors." To the 144-year-old insurance titan, the 3-year-old startup's value lies in its online-first platform, efficient data collection and artificial intelligence to find personalized insurance at a much faster rate, executives said.

Four years ago, more than 700 computer scientists competed in a contest to build artificial intelligence that could pass an eighth-grade science test. There was $80,000 in prize money on the line. Even the most sophisticated system couldn't do better than 60% on the test. AI couldn't match the language and logic skills that students are expected to have when they enter high school. But Wednesday, the Allen Institute for Artificial Intelligence, a prominent lab in Seattle, unveiled a new system that passed the test with room to spare.

Facebook Inc.'s new dating feature has led to at least one important match: The marriage of users' Facebook and Instagram profiles for the first time. The social giant announced Thursday that it is launching its nascent dating service in the U.S.--a feature that's been tested in 19 other countries since it was unveiled in May 2018. Users can combine elements of their Facebook and Instagram accounts to create a separate dating profile--the most notable way Facebook has ever combined the two entities from a product perspective. Facebook Dating will exist inside Facebook's main app, but users will be able to pull photos from their Instagram profiles into their dating profile, and by the end of the year be able to publish their Instagram Stories to their dating profile as well. Facebook daters can also use their Instagram followers for a feature called "Secret Crush," which could match them with another Facebook Dating user from their Instagram network.

The Oldest House is a maze of concrete and paperwork. It's a building full of secrets, stuffed top to bottom with government bureaucrats, some of whom are now host to an interdimensional plague. The Oldest House is an office building in the heart of New York City, but it's also a portal to everywhere and all the spaces between. It's a place where office drones file paperwork. It's a place where the U.S. government researches the "paranatural."

The social media giant is putting $10 million into the "Deepfake Detection Challenge," which aims to spur detection research. As part of the project, Facebook is commissioning researchers to produce realistic deepfakes to create a data set for testing detection tools. The company said the videos, which will be released in December, will feature paid actors and that no user data would be utilized. In the run-up to the U.S. presidential election in November 2020, social platforms have been under pressure to tackle the threat of deepfakes, which use artificial intelligence to create hyper-realistic videos where a person appears to say or do something they did not. While there has not been a well-crafted deepfake video with major political consequences in the United States, the potential for manipulated video to cause turmoil was recently demonstrated by a "cheapfake" clip of House Speaker Nancy Pelosi, manually slowed down to make her speech seem slurred.

In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information. In the differential privacy framework, we develop a differentially private ridge estimator by perturbing the sample covariance matrix. Then we develop a differentially private graphical lasso estimator by using the alternating direction method of multipliers (ADMM) algorithm. The theoretical results and empirical results that show the utility of the proposed methods are also provided. Keywords differential privacy, graphical model, ADMM algorithm 1 Introduction Precision matrix plays a fundamental role in many statistical inference problems. For example, in discriminant analysis, the precision matrix needs to be estimated to compute the classification rules[1]. In graphical models, the structure exploration of gaussian graphical model is equivalent to recover the support of the precision matrix[2]. Moreover, the precision matrix is useful for a wide range of applications including portfolio optimization, genomics and single processing, among many others. Therefore, it is of great importance to estimate the precision matrix.

Computer programs are composed of three fundamental mechanisms: elementary operations, logical flow control and memory usage. In the history of neural networks [19] only the use of elementary operations have been extensively explored since so far, but during the last few years the coupling with an external piece of memory is gaining popularity [24]. Neural Turing Machines (NTMs) were developed in 2014 at Google DeepMind Labs [8] in an attempt to couple a neural network with an external memory component in order to improve long-term dependency learning in sequences prediction. Although recurrent neural networks (RNNs) are Turing-complete on their own [20], the difficulties that arise during their training (like the vanishing or the exploding gradient problems [18, 15]) prevented them from being employed in learning more complex tasks, for example algorithmic ones [27]. NTMs derive their name from the analogy with standard Turing Machines (TMs) [22] in addressing an infinite (or at least large enough to be considered so) portion of memory with an attentional mechanism similar to the read/write head of a TM. In contrast to a standard TM, a NTM is a "differentiable computer" that can be trained using gradient descent methods and can therefore learn its own "program" independently (attempts using Neuroevolution [9] and reinforcement learning [26] have also been made). In human brains, the most similar process to an algorithm is the concept of "working memory" [1]: this mechanism allows the brain to rapidly create "variables" [11] by storing short-term information and manipulating them in a rulebased way [17]. The analogy with an algorithm is evident, and a NTM is similar to this process because it can learn tasks in which it is required to manipulate rapidly-created variables. Also the attention mechanism in a NTM is similar to the way the working memory bounds its information in certain slots of memory in the brain [6], despite the fact that a NTM autonomously learns how to do that.

In this paper, we aimed at reviewing present literature on employing nonlinear analysis in combination with machine learning methods, in depression detection or prediction task. We are focusing on an affordable data-driven approach, applicable for everyday clinical practice, and in particular, those based on electroencephalographic (EEG) recordings. Among those studies utilizing EEG, we are discussing a group of applications used for detecting the depression based on the resting state EEG (detection studies) and interventional studies (using stimulus in their protocols or aiming to predict the outcome of therapy). We conclude with a discussion and review of guidelines to improve the reliability of developed models that could serve the improvement of diagnostic and more accurate treatment of depression.

K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages, such as the positions of the initial clustering centres (centroids), which can greatly affect the clustering solution. Over the years many K-Means variations and initialisations techniques have been proposed with different degrees of complexity. In this study we focus on common K-Means variations and deterministic initialisation techniques and we first show that more sophisticated initialisation methods reduce or alleviates the need of complex K-Means clustering, and secondly, that deterministic methods can achieve equivalent or better performance than stochastic methods. These conclusions are obtained through extensive benchmarking using different model data sets from various studies as well as clustering data sets.